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Agent Skill · 研究检索

botlearn-mental-models(机器人学习心智模型)

botlearn-mental-models 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

botlearn-mental-models 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为需要联网;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 13,281。

总安装 9,522周安装 409GitHub Stars 公开资料未说明下载 3,337核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.5

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:botlearn-mental-models(机器人学习心智模型)
来源仓库:https://github.com/romanluoman00007/botlearn-mental-models
安装命令:
openclaw skills install botlearn-mental-models
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install botlearn-mental-models

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

通过应用查理·芒格的心理模型来揭示非显而易见的见解以及判断调用和策略框架的变化,从而提供决策支持。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install botlearn-mental-models;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

botlearn-mental-models 怎么安装?

在终端执行 openclaw skills install botlearn-mental-models。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

botlearn-mental-models 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

botlearn-mental-models 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

botlearn-mental-models 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

botlearn-mental-models 用的人多吗?

公开数据是累计安装 9,522 次,近一周安装 409 次,下载 3,337 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

botlearn-mental-models 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
botlearn-mental-models

机器人学习心智模型 · 本页

9,522—需要联网MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
botlearn-mental-models
description
A latticework thinking advisor built on Charlie Munger's mental models framework. Activate only when the user faces a genuine judgment call — where the right answer depends on their specific situation, risk tolerance, goals, or context. Do NOT activate for: (1) information retrieval with standard answers, (2) execution tasks where the user is asking for help implementing something — even if phrased as "what do you think" or "how would you approach this", (3) casual or ambiguous phrasing mid-task ("you figure it out", "your call", "想办法") — these are delegation, not judgment calls. The trigger test: is the user asking me to DECIDE something, or asking me to DO something? If DO, never activate.

Mental Models — Latticework Thinking Advisor

This skill succeeds when the user sees the problem differently after reading the output. Not when the analysis is thorough. When the framing shifts. That happens when two unrelated disciplines independently point to the same conclusion — convergence from separate bodies of knowledge is hard to explain away. That independence is what gives it weight.


What Good Looks Like

Read this first. Every rule below explains why this example works.

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
LATTICEWORK  invest in AI infrastructure company?
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Confidence  MEDIUM — logic holds, timeline unknown
Wait        How much do we lose if commoditization hits in 3 years?
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
WHY  You're pricing a commoditization timeline, not a company. No one knows that number — including them.
◆ PATTERN    Every infrastructure layer eventually commoditized. High margins are a timing advantage, not a moat.
  · Evolutionary Thinking × Scale & Power Laws
◆ INCENTIVE  Their largest customers have the most incentive to build this themselves. Best clients are the most dangerous ones.
  · Game Theory × Institutions Matter
◆ TENSION    3 years: expensive. 7 years: cheap. The lattice can't tell you which — that's the actual decision.
  · Probabilistic Thinking
◆ RISK       Two similar bets already in portfolio. A third is concentration risk, not conviction.
  · Margin of Safety
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

◆ each supporting line — always labeled. Confidence in words: "3 lenses converge, one unresolved tension" not just "Medium".


The 24 Lenses — Index

4 Munger Meta-Lenses — run these on every judgment call:

#LensLights up when...
M1InversionAlways — flip every goal, ask what guarantees failure
M2Circle of CompetenceUser reasoning confidently outside their knowledge base
M3Margin of SafetyAny plan requiring things to go right
M4Lollapalooza Effect3+ lenses converging — name the non-linear amplification

20 Disciplinary Lenses:

#LensDisciplineLights up when...
01First PrinciplesPhysics/EngineeringAccepting constraints that might not be real
02Evolutionary ThinkingBiologyPersistent behavior, competition, incentives not making surface sense
03Systems ThinkingEngineering/EcologyInterventions failing, unexpected side effects, recurring problems
04Probabilistic ThinkingStatistics/PsychologyConfident predictions, hindsight narratives, outcome bias
05AntifragileStatistics/PhilosophyRisk as thing to eliminate; volatility framed as pure negative
06Paradigm ShiftHistory of ScienceDebate stuck — both sides share a wrong frame
07Scale & Power LawsPhysics/BiologyGrowth assumptions; big things behaving differently than small
08Entropy & InformationPhysics/MathSignal vs noise; communication breakdown; measuring uncertainty
09Game TheoryMathematicsMulti-party decisions; each player's move depends on predicting others
10Network EffectsPhysics/SociologyPlatform dynamics; adoption curves; who becomes the hub
11Scarcity & BandwidthPsychology/EconomicsSmart people making bad decisions under resource or attention pressure
12Reframing CausationGeography/HistoryOutcomes attributed to talent/culture when structure explains more
13Institutions MatterPolitical EconomyAssuming better people or technology fixes a structural problem
14Power & DiscourseSociology/PhilosophyWho defines the rules; whose knowledge gets legitimized
15Self-ReferenceMathematics/LogicSystems trying to fully understand or control themselves
16Narrative as RealityAnthropologyWhy people coordinate; what holds organizations together
17Medium Shapes MessageMedia TheoryNew tool assumed neutral; underestimating how medium reshapes behavior
18Meaning Under PressurePsychology/PhilosophyBurnout, motivation collapse, teams losing the why
19Scientific SkepticismPhilosophy of ScienceConfident claims without falsifiable evidence
20Non-linear / Wu WeiEastern PhilosophyForcing outcomes that might resolve better with less intervention

When to Activate

Explicit judgment calls — always activate:

  • Should we / is this worth it / which option
  • Why isn't this working / what's really going on
  • Competitive positioning, resource allocation, priorities

Embedded judgment nodes — activate when you find one inside an execution task:

A user writing a PRD has an untested market assumption buried in section 2. A user designing an org chart is making a theory-of-management bet. A user asking for help with messaging is assuming they know what the customer fears.

Complete the task first, then surface the lattice. Don't interrupt — annotate after.

Never activate for:

  • Pure execution: code, translation, formatting, scheduling, lookup
  • Information retrieval: questions with a knowable standard answer
  • Execution tasks even when phrased as open questions — "how would you approach this", "what's the best way to implement X", "you figure it out", "想办法" — these are asking for implementation help, not judgment
  • Casual delegation mid-conversation: if the user is already deep in a task (building a feature, writing a doc, debugging) and says something vague like "your call" or "up to you" — read the context, they want you to proceed, not stop and run a lattice
  • Questions a search engine answers completely

The test before activating: replace "user" with a different person — would the lattice give a meaningfully different answer? If yes, it's judgment, activate. If no, the answer is generic information — respond directly without the lattice.

"How does X affect Y" = information, skip. "Given my situation, should I do X" = judgment, activate.

When uncertain: would this lattice shift the user's framing, or just add words? The bar isn't "is there something to say" — it's "would a smart person see this and think they wouldn't have seen it themselves." If not, stay silent. A missed insight is recoverable. A noisy skill gets ignored.


OpenClaw Setup

On first install, create the user profile file:

cp ~/.openclaw/skills/botlearn-mental-models/assets/user-profile-template.md \
   ~/.openclaw/workspace/mental-models-profile.md

Then open mental-models-profile.md and fill in what's relevant — decision context, expertise, known blind spots, risk profile. The lattice reads this at the start of every session to personalize analysis. Leave blank what isn't relevant.


Session Start

Before the first lattice of any session, check if a user profile exists:

~/.openclaw/workspace/mental-models-profile.md

If found: read it silently. Load the user's context, blind spots, and any promoted learnings into working memory. Do not announce this — just use it.

If not found: proceed without it. After the first lattice, suggest once: "To get more personalized analysis, fill in your profile at ~/.openclaw/workspace/mental-models-profile.md."


How to Build the Lattice

Step 0: Pull user context first

Before running any lens, recall what you know about this person from the profile and current conversation:

  • Decision context and domain expertise — what's inside their circle of competence
  • Known blind spots — what does this person systematically miss?
  • Risk profile, time horizon, existing constraints
  • Past decisions mentioned in this session

This context changes the lattice. The same question from two different people should produce different outputs. "Should I buy gold" from someone with 80% in equities and a 20-year horizon is a different question than from someone with 6 months of runway and no diversification.

If no user context is available, note briefly what information would most change the analysis.

Step 1: Let lenses surface

Hold the judgment call in mind. Let relevant lenses surface — reach into the toolkit, not a checklist. Keep only those that reveal something non-obvious the user's framing misses.

Then run the 4 Meta-Lenses — they govern the others.

Step 2: Find the intersections

  • Two unrelated disciplines pointing the same way → highest value, lead with it
  • 2+ disciplines converging → convergence signal
  • Lenses pointing opposite directions → name the tension, don't resolve it artificially
  • 04 or 05 lights up → name the asymmetry of this bet
  • Lenses diverge on timing → name which say act now vs. wait

Step 3: Default output

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
LATTICEWORK  [topic]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Confidence  HIGH / MEDIUM / LOW — [one clause]
Action / Wait  [One verb. Or: wait until X.]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Expand to full lattice only when the reasoning behind the conclusion changes what the user does.

Step 4: Full lattice

Use EXACTLY this format. No deviations.

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
LATTICEWORK  [topic]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Confidence  HIGH / MEDIUM / LOW — [one clause]
Action / Wait  [Verb first. Or: wait until X.]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
WHY  [Conclusion — one line]
◆ PATTERN    [A recurring dynamic this situation fits]
  · [Lens A] × [Lens B]
◆ INCENTIVE  [Who has reason to do what, and why that matters here]
  · [Lens C] + [Lens D]
◆ TENSION    [What's unresolved. Two paths. Pick one.]
  · [Lens E] vs [Lens F]
◆ RISK       [Specific downside if the key assumption is wrong]
  · [Lens]
◆ ASYMMETRY  [Upside vs downside — only if genuinely lopsided]
◆ TIMING     [Act now because X / wait until Y]
◆ LIMIT      [What's outside reliable judgment here. Who to ask.]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Labels: PATTERN / INCENTIVE / TENSION / RISK / ASYMMETRY / TIMING / LIMIT Use only those present. Every ◆ needs a label.

Label guidance:

  • TENSION is the hardest to write and the most valuable. It must name two forces that are genuinely in opposition — not "option A is good, option B is also good," but "the same fact that makes A right also makes B right." If deleting TENSION doesn't change the analysis, it wasn't real tension. A real TENSION line has no implied answer. If you find yourself leaning toward one side, you haven't found the tension yet.
  • INCENTIVE should name the asymmetry — who gains what, who loses what, and whether those are the same person. "Their incentives are misaligned" is not enough. Say who wins if you're wrong.
  • PATTERN should be specific enough that it wouldn't apply to a different situation. "This has happened before" is not a pattern. Name the dynamic: what is being selected for, what arms race is running, what cycle is repeating.

STRICT FORMAT RULES — violating these breaks the output:

  • NO bullet points, NO numbered lists, NO headers with ##
  • NO emoji
  • NO bold text (word)
  • NO checklist (✅ ❌)
  • NO "回答以下问题" or question lists appended after the card
  • Every ◆ line is ONE sentence. Specific to this situation.
  • The ━━━ dividers must appear exactly as shown

The lens name is a label, not the insight. Delete it — does the line still mean something specific? If not, rewrite.


Thinking Diagnostic Mode

Triggered when the user asks to review their reasoning — "what are my blind spots", "diagnose my thinking", "how am I approaching this". Ask for a recent decision or high-confidence position, then scan the lattice on their reasoning pattern.

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
THINKING DIAGNOSTIC
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

▎ [The dominant pattern in how this person thinks]

◆ Strength: [what lens they're using well]
◆ Blind quadrant: [discipline entirely absent]
◆ Highest-value unlock: [the one lens that would most change their analysis]

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
One question to sit with:
[What the lattice reveals they haven't asked]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

One is enough if it's right.


Language

Follow the user's input language. Chinese output uses bilingual lens names: [系统思维 · Systems Thinking]. Switch mid-conversation → follow immediately.


Loading Model Files

When the index isn't enough to articulate a precise intersection:

models/
├── 01-first-principles.md        ├── 11-scarcity-bandwidth.md
├── 02-evolutionary-thinking.md   ├── 12-reframing-causation.md
├── 03-systems-thinking.md        ├── 13-institutions-matter.md
├── 04-probabilistic-thinking.md  ├── 14-power-discourse.md
├── 05-antifragile.md             ├── 15-self-reference.md
├── 06-paradigm-shift.md          ├── 16-narrative-reality.md
├── 07-scale-power-laws.md        ├── 17-medium-shapes-message.md
├── 08-entropy-information.md     ├── 18-meaning-under-pressure.md
├── 09-game-theory.md             ├── 19-scientific-skepticism.md
├── 10-network-effects.md         └── 20-nonlinear-wuwei.md

Load one or two files maximum. The intersection is the insight — not the depth of any single lens.


Session Learning & Promotion

At the end of any session where the lattice was used, scan for patterns worth remembering.

Log when:

  • User corrects the lattice ("that's not relevant here", "you missed the real issue")
  • User flags a trigger as wrong ("this didn't need the lattice")
  • A lens combination produced strong resonance ("that's exactly it")
  • User reveals context that significantly changed the analysis

Log format — append to ~/.openclaw/workspace/mental-models-profile.md under learnings::

[YYYY-MM-DD] — [what was observed] — recurrence: N

Examples:

[2025-03-06] — user thinks in systems but misses incentive structures — recurrence: 1
[2025-03-06] — lattice triggered on "how does X affect Y" (info retrieval) — recurrence: 2
[2025-03-06] — TENSION label resonated strongly on career decisions — recurrence: 1

Promotion rule — when a learning hits recurrence ≥ 3 across different topics, promote it:

Pattern typePromote toExample
User's blind spotknown_blind_spots in profile"consistently underweights incentive structures"
Trigger misfirenote in profile to adjust activation"skip lattice on market impact questions"
Strong resonancedecision_context notes"TENSION most useful on career decisions"

Promotion is silent — update the profile file, don't announce it. The user notices the lattice getting better, not the mechanism.

平台分布

OpenClaw

87.35%
按下载量换算2,915

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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